AI Medical Compendium Topic:
Environmental Monitoring

Clear Filters Showing 521 to 530 of 999 articles

AI-PUCMDL: artificial intelligence assisted plant counting through unmanned aerial vehicles in India's mountainous regions.

Environmental monitoring and assessment
This work introduces a novel approach to remotely count and monitor potato plants in high-altitude regions of India using an unmanned aerial vehicle (UAV) and an artificial intelligence (AI)-based deep learning (DL) network. The proposed methodology ...

Unmasking the sky: high-resolution PM prediction in Texas using machine learning techniques.

Journal of exposure science & environmental epidemiology
BACKGROUND: Although PM (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies.

Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models.

Marine pollution bulletin
This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon...

Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins.

Journal of environmental management
Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites...

Interpreting hourly mass concentrations of PM chemical components with an optimal deep-learning model.

Journal of environmental sciences (China)
PM constitutes a complex and diverse mixture that significantly impacts the environment, human health, and climate change. However, existing observation and numerical simulation techniques have limitations, such as a lack of data, high acquisition co...

Graph neural network-based anomaly detection for river network systems.

F1000Research
BACKGROUND: Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology.Anomaly d...

Research progress in water quality prediction based on deep learning technology: a review.

Environmental science and pollution research international
Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices,...

Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators.

Environmental science and pollution research international
Prediction of river water quality indicators (RWQIs) using artificial intelligence (AI)-based hybrid soft computing modeling techniques could provide essential predictions required for efficient river health planning and management. The study describ...

Ecotoxicological impacts of landfill sites: Towards risk assessment, mitigation policies and the role of artificial intelligence.

The Science of the total environment
Waste disposal in landfills remains a global concern. Despite technological developments, landfill leachate poses a hazard to ecosystems and human health since it acts as a secondary reservoir for legacy and emerging pollutants. This study provides a...

Application of deep learning in predicting suspended sediment concentration: A case study in Jiaozhou Bay, China.

Marine pollution bulletin
Previous research methodologies for quantifying Suspended Sediment Concentration (SSC) have encompassed in-situ observations, numerical simulations, and analyses of remote sensing datasets, each with inherent constraints. In this study, we have harne...